Precision immunoprofiling to reveal diagnostic biomarkers of latent TB infection

精确免疫分析揭示潜伏结核感染的诊断生物标志物

• 批准号: 10247473
• 负责人: Ryan C Bailey
• 金额: $ 74.65万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-05 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10247473
• 关键词: Affect; Algorithms; American; Antibiotic Therapy; Antibiotics; Antigens; Bioinformatics; Biological Assay; Biological Markers; Clinical; Clinical Treatment; Collaborations; Complex; Cytokine Network Pathway; Detection; Development; Diagnosis; Diagnostic; Disease; Disease Management; Eligibility Determination; Generations; Goals; Gold; Health Personnel; Immune; Immune response; Immunocompetent; Immunologic Markers; Immunologic Memory; Immunologic Monitoring; Individual; Infection; Inflammatory; Informatics; Interferons; International; Location; Machine Learning; Measurement; Measures; Modeling; Patients; Peripheral; Peripheral Blood Mononuclear Cell; Plasma; Population; Predictive Value; Prevention strategy; Regimen; Residual state; Risk; Sampling; Schedule; Silicon; Specificity; Stratification; Technology; Testing; Therapeutic Intervention; Translations; Tuberculin Test; Tuberculosis; Whole Blood; antigen challenge; base; bioinformatics tool; biomarker signature; clinical Diagnosis; clinical practice; cytokine; data streams; diagnostic accuracy; diagnostic biomarker; feature selection; high risk; immune function; immunoregulation; improved; individual variation; latent infection; machine learning algorithm; model development; monocyte; mortality; novel diagnostics; novel strategies; patient stratification; personalized approach; personalized diagnostics; photonics; precision medicine; predictive marker; predictive modeling; prevent; prognostic; prospective; response; screening; side effect; targeted treatment; tool; treatment strategy; tuberculosis treatment

PROJECT SUMMARY Tuberculosis (TB) is among the leading causes of mortality worldwide with an estimated 2 billion individuals currently infected. Latent tuberculosis infection (LTBI) is the most common form of TB infection affecting 13 million Americans. While many with LTBI remain asymptomatic, an estimated 10% of immunocompetent patients with LTBI will reactivate to active TB, and will become infectious. LTBI is treatable with a prolonged antibiotic treatment; however, potential side effects motivate the development of new diagnostic approaches that can identify with high specificity patients at the highest risk of reactivation, for who therapy would be most beneficial. The tuberculin skin test (TST) and interferon-γ release assays (IGRAs) are commonly used for TB and LTBI screening. Both tests provide good measures of TB exposure; however, neither is effective at diagnosing LTBI (positive predictive values <5%). Moreover, neither provide any prognostic stratification based upon reactivation risk. Both the TST and IGRAs probe immunological memory to TB-related antigen challenges and we hypothesize that a more nuanced and personalized approach to monitoring immune responses to both TB- specific and non-specific antigens might reveal new approaches to LTBI diagnosis and patient stratification. Enabling a new, individualized approach to LTBI diagnostics, we propose to combine high throughput, multiplexed inflammatory biomarker detection strategies and powerful bioinformatics tools that allow for the identification of previously obscured multi-marker diagnostic signatures of LTBI status and reactivation risk. Silicon photonic microring resonators are an enabling technology for biomarker analysis due to their intrinsic scalability and multiplexing capabilities. Applied to the detection of cytokine panels, this technology supports the rapid immune profiling of individual samples under both TB-specific and non-specific antigen stimulation conditions. Machine learning algorithms will be utilized to analyze the resulting dense data streams to facilitate selection of key diagnostic signatures forming the basis for predictive model development and deployment. This powerful analytical combination is supplemented by deep expertise in clinical diagnosis and treatment of TB and LTBI, and an enabling collaboration and connection to subjects from an international location with high TB burden and exposure in a healthcare worker population subjected to regularly-scheduled and repeated LTBI screening. The resulting diagnostic workflow and machine learning feature selection approaches will reveal multiplexed biomarker signatures that have strong positive predictive correlation with LTBI status (+ or -). This approach will also further stratify LTBI+ subjects on the basis of reactivation potential, thus providing a fundamentally new approach to identifying subjects that are most likely to benefit from therapeutic intervention. The end result of this project will be a new precision medicine-based diagnostic strategy that is vastly superior to the current state-of-the-art and offers the potential to transform current clinical practice.

项目概要 结核病 (TB) 是全球导致死亡的主要原因之一，估计有 20 亿人患有结核病 目前已感染潜伏性结核感染 (LTBI)，这是最常见的结核感染形式，影响 13 人。 尽管许多 LTBI 患者仍无症状，但估计有 10% 的免疫功能正常的患者。 患有 LTBI 的患者会重新激活为活动性结核病，并且会变得具有传染性，可以用长期抗生素治疗。 然而，潜在的副作用促使人们开发新的诊断方法 识别出重新激活风险最高的高特异性患者，对他们来说治疗最有益。 结核菌素皮试 (TST) 和干扰素 γ 释放试验 (IGRA) 通常用于检测 TB 和 LTBI 这两种测试都可以很好地衡量结核病暴露情况；然而，这两种测试都不能有效诊断 LTBI。 （阳性预测值<5%）此外，两者都没有提供基于重新激活的任何预后分层。 TST 和 IGRA 都探测结核病相关抗原挑战的免疫记忆，我们 追求一种更细致和个性化的方法来监测对结核病和结核病的免疫反应 特异性和非特异性抗原可能揭示 LTBI 诊断和患者分层的新方法。 我们建议将高通量、 多重炎症生物标志物检测策略和强大的生物信息学工具，允许 识别先前模糊的 LTBI 状态和再激活风险的多标志物诊断特征。 硅光子微环谐振器由于其固有的特性而成为生物标志物分析的支持技术 该技术应用于细胞因子组的检测，支持可扩展性和多重功能。 在结核病特异性和非特异性抗原刺激下对单个样本进行快速免疫分析 机器学习算法将用于分析产生的密集数据流，以促进。 选择构成预测模型开发和部署基础的关键诊断特征。 强大的分析组合辅以结核病临床诊断和治疗方面的深厚专业知识 LTBI，以及与来自结核病高负担的国际地点的受试者进行有利的合作和联系 定期和重复进行 LTBI 筛查的医护人员群体中的暴露情况。 由此产生的诊断工作流程和机器学习特征选择方法将揭示多路复用 与 LTBI 状态（+ 或 -）具有强正预测相关性的生物标志物特征也将采用这种方法。 根据再激活潜力进一步对 LTBI+ 受试者进行分层，从而提供了一种全新的方法 确定最有可能从治疗干预中受益的受试者。该项目的最终结果将是 新的基于精准医学的诊断策略，远远优于当前最先进的技术，并提供 改变当前临床实践的潜力。